Advertisement

Information Entropy and Interaction Optimization Model Based on Swarm Intelligence

  • Xiaoxian He
  • Yunlong Zhu
  • Kunyuan Hu
  • Ben Niu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)

Abstract

By introducing the information entropy H(X) and mutual information I(X;Y) of information theory into swarm intelligence, the Interaction Optimization Model (IOM) is proposed. In this model, the information interaction process of individuals is analyzed with H(X) and I(X;Y) aiming at solving optimization problems. We call this optimization approach as interaction optimization. In order to validate this model, we proposed a new algorithm for Traveling Salesman Problem (TSP), namely Route-Exchange Algorithm (REA), which is inspired by the information interaction of individuals in swarm intelligence. Some benchmarks are tested in the experiments. The results indicate that the algorithm can quickly converge to the optimal solution with quite low cost.

Keywords

Particle Swarm Optimizer Mutual Information Travel Salesman Problem Travel Salesman Problem Information Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence-from Natural to Artificial System. Oxford University Press, New York (1999)Google Scholar
  2. 2.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Grefenstette, J., Gopal, R., Rosimaita, B., Van Gucht, D.: Genetic algorithms for the traveling salesman problem. In: Proceedings of the International Conference on Genetics Algorithms and their Applications, pp. 160–168 (1985)Google Scholar
  4. 4.
    Yao, X.: Evolutionary Computation: Theory and Applications. World Scientific, Singapore (1999)Google Scholar
  5. 5.
    Tan, K.C., Lim, M.H., Yao, X., Wang, L.P. (eds.): Recent Advances in Simulated Evolution and Learning. World Scientific, Singapore (2004)MATHGoogle Scholar
  6. 6.
    Liu, J., Zhong, W.C., Liu, F., Jiao, L.C.: Organizational coevolutionary classification algorithm for radar target recognition. Journal of Infrared and Millimeter Waves 23(3), 208–212 (2004)Google Scholar
  7. 7.
    Han, J., Cai, Q.S.: Emergent Intelligence in AER Model. Chinese Journal of Pattern Recognition and Artificial Intelligence 15(2), 134–142 (2002)Google Scholar
  8. 8.
    Shannon, C.E.: A mathematical theory of communication. Bell System Technology Journal 27, 397–423 (1948)MathSciNetGoogle Scholar
  9. 9.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  10. 10.
  11. 11.
    Niu, B., Zhu, Y.-l., He, X.-X.: Multi-population Cooperative Particle Swarm Optimization. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 874–883. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Niu, B., Zhu, Y.-l., He, X.-X.: A Multi-population Cooperative Particle Swarm Optimizer for Neural Network Training. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 570–576. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaoxian He
    • 1
    • 2
  • Yunlong Zhu
    • 1
  • Kunyuan Hu
    • 1
  • Ben Niu
    • 1
    • 2
  1. 1.Shenyang Institute of AutomationChinese Academy of SciencesShenyang
  2. 2.Graduate school of the Chinese Academy of SciencesBeijing

Personalised recommendations